Seizures following a nerve agent (NA) attack induce a time-critical emergency which can lead to sudden death or severe morbidity. Survival is dependent upon early and aggressive therapy. Seizures may not be evident from physical examination, and emergency personnel typically cannot interpret the electroencephalogram. Thus, as noted in the RFA, there is a pressing need for automated EEG interpretation and detection of epileptiform activity. In order to respond to this need, we assembled an interdisciplinary team from Infinite Biomedical Technologies, LLC, the United States Army Medical Research Institute of Chemical Defense (USAMRICD), and the Johns Hopkins School of Medicine. Together, the team has broad experience with NA-induced neuropathology, clinical management of seizures, quantitative neurodiagnostics, and hardware design. Based on this experience, we propose the development of a portable system for automated seizure detection. It includes a wireless headband and a handheld device which features a Chemical Seizure Vector (CSV) algorithm. The CSV is a "digital fingerprint" based on 5 spectral and temporal analyses specifically selected to recognize seizure morphology. This combinatorial method creates a 5-dimensional vector to reliably classify and discriminate seizure waveforms. Noise-reduction and artifact rejection are incorporated both at the level of the individual algorithm and upon simultaneous consideration of the individual analyses in 5-dimensional space. During Phase I we will evaluate the ability of CSV to detect the presence of seizures. Given that it is not feasible to study nerve agent exposure in patients, we will employ two complementary models. First, we will use an established primate model which incorporates exposure to a nerve agent. Second, we will use a human model involving seizure patients of a non-NA-associated etiology. We will investigate the performance of the individual component algorithms and the combined CSV using Receiver Operator Characteristic (ROC) curves. The primary milestone for Phase I is creation of an optimal chemical seizure detection algorithm based on the analysis of the ROCs. Phase II of this Fast-track application involves further refinement of CSV to produce a three-level indicator. We will discriminate seizures (RED) from peri-ictal activity (YELLOW) and the normal EEG (GREEN). CSV will be tested in a prospective evaluation in the primate model and in human seizure patients. The technology will be packaged into a system which features a self-adhesive electrode headband and a handheld device. Finally, the entire system will be validated in a realistic setting during live chemical emergency drills. Nerve agent exposures are a serious threat and can cause brain injury and death. We will develop an automated system to diagnose EEG seizure activity in the brain to help care for attack victims.